Complexity of AI
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Complexity".
Deadline for manuscript submissions: 31 August 2026 | Viewed by 214
Special Issue Editors
Interests: science of science; complex systems; network science; critical transitions; early warnings; agent-based modeling; econophysics; temporal networks
Special Issues, Collections and Topics in MDPI journals
2. Department of Physics, Faculty of Science, National University of Singapore, Singapore
Interests: complex systems; dynamical systems; deep learning dynamics; network science; complexity theory
Special Issues, Collections and Topics in MDPI journals
Interests: quantum systems; biological systems; social systems (social-ecological and social-economic); urban systems; health systems; climatic systems; foundation of complex systems
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Modern artificial intelligence (AI) models, specifically artificial neural networks, are typically complex systems that consist of a large number of interactions. With the accelerating progress made in the capabilities of AI, there is a growing need for a better understanding of their working principles. Complexity science, the field underlying Giorgio Parisi’s work which won the 2021 Nobel prize in physics and providing some of the key theoretical foundations for the AI contributions presented by John J. Hopfield and Geoffrey Hinton, which won the 2024 Nobel prize, is receiving increasing attention in relation to the pursuit of an understanding of AI models.
As complex systems, AI models typically exhibit the phenomena of phase transition, emergence (of intelligence), multiple meta-stabilities, chaos, self-organization, etc. Studying and analyzing AI models from the perspective of complexity science can pave the way for a deeper understanding of how AI models process information, carry out logic reasoning, and generate new data like images or text. Such findings can potentially help us to improve the capabilities, safety, and efficiency of AI models through untangling their complexities.
This Special Issue is organized in conjunction with the Focused Session entitled “Complexity of AI” at the Asia-Pacific Summer School and Conference on Networks and Complex Systems (APCNCS) 2026, held in Singapore (https://apcncs2026.github.io/). Authors are invited to contribute to both the Special Issue and the Focused Session. We welcome original research exploring the complexity of AI models from diverse perspectives, including methods, theories, applications, and empirical studies.
Topics of interest in this special issue:
- Emergence and phase transitions in AI/ML models
- Statistical mechanics of neural networks
- Dynamics of neural networks training and inference
- Power laws in AI/ML models
- Self organization in neural networks
- Energy based models and analysis
- Reinforcement learning in multi-agent systems
- Adaptive and causal ML and AI
You may choose our Joint Special Issue in Complexities.
Dr. Siew Ann Cheong
Dr. Ling Feng
Dr. Lock Yue Chew
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- complexity science
- artificial intelligence
- neural networks
- statistical physics
- phase transitions
- dynamical systems
- self-organization
Benefits of Publishing in a Special Issue
- Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
- Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
- Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
- External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
- Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.
Further information on MDPI's Special Issue policies can be found here.
